OPAL: A Knowledge-Based System for Industrial Job-Shop Scheduling

1988 ◽  
pp. 295-330 ◽  
Author(s):  
E. Bensana ◽  
G. Bel ◽  
D. Dubois
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Shop Scheduling ◽  
Knowledge Based System ◽  
Knowledge Based

A knowledge based job-shop scheduling system with controlled backtracking

1993 ◽  
Vol 24 (3) ◽  
pp. 391-400 ◽  
Author(s):  
Odesseus Charalambous ◽  
Khalil S. Hindi
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling System ◽  
Shop Scheduling ◽  
Knowledge Based

A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems

2010 ◽  
Vol 10 (3) ◽  
pp. 888-896 ◽  
Author(s):  
Li-Ning Xing ◽  
Ying-Wu Chen ◽  
Peng Wang ◽  
Qing-Song Zhao ◽  
Jian Xiong
Keyword(s):  
Ant Colony Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Ant Colony ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Knowledge Based

scholarly journals A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Hamed Piroozfard ◽  
Kuan Yew Wong ◽  
Adnan Hassan
Keyword(s):  
Genetic Algorithm ◽  
Production Management ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Neighborhood Search ◽  
Function Evaluation ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Knowledge Based

Scheduling is considered as an important topic in production management and combinatorial optimization in which it ubiquitously exists in most of the real-world applications. The attempts of finding optimal or near optimal solutions for the job shop scheduling problems are deemed important, because they are characterized as highly complex andNP-hard problems. This paper describes the development of a hybrid genetic algorithm for solving the nonpreemptive job shop scheduling problems with the objective of minimizing makespan. In order to solve the presented problem more effectively, an operation-based representation was used to enable the construction of feasible schedules. In addition, a new knowledge-based operator was designed based on the problem’s characteristics in order to use machines’ idle times to improve the solution quality, and it was developed in the context of function evaluation. A machine based precedence preserving order-based crossover was proposed to generate the offspring. Furthermore, a simulated annealing based neighborhood search technique was used to improve the local exploitation ability of the algorithm and to increase its population diversity. In order to prove the efficiency and effectiveness of the proposed algorithm, numerous benchmarked instances were collected from the Operations Research Library. Computational results of the proposed hybrid genetic algorithm demonstrate its effectiveness.

KBSS: a knowledge-based job-shop scheduling system*

1993 ◽  
Vol 4 (4) ◽  
pp. 304-310 ◽  
Author(s):  
O. CHARALAMBOUS ◽  
K. S. HINDI
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling System ◽  
Shop Scheduling ◽  
Knowledge Based
Sign in / Sign up

Export Citation Format

Share Document